Investigating GPS Signals Indoors with Extreme High-Sensitivity Detection Techniques
Bibliographic record
Abstract
ABSTRACT: High-sensitivity GPS and assisted GPS are being extensively researched as methods to improve positioning indoors, where weak, multipath-affected signals are often difficult or impossible to use. To improve knowledge of indoor GPS behavior, this paper presents details of a raw GPS processing technique that enables extremely long coherent integrations, thereby providing extremely high detection sensitivity for indoor signals. The technique is used to evaluate signal characteristics in a pair of datasets gathered indoors, with carrier-to-noise density ratios as much as 40 dB or more below nominal open-sky signals. Results show that weak signals such as these can be used to provide reasonably accurate positioning if a sufficient number of signals can be detected to ensure good positioning geometry. Signal degradations caused by multipath are shown to be less damaging to position than the loss of availability caused by low signal strength. In addition, the high-sensitivity techniques based on precise tracking loop control demonstrate the potential for improved high-sensitivity GPS-based technologies using ultra-tight integration with additional sensors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".